immunoClust EM-iteration on Cell-events given initial Cluster Membership Assignment

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Description

Performs an EM-iteration on cell event observations given an initial cluster membership for the cell events and returns the fitted cluster information in an object of class immunoClust.

Usage

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cell.ME(data, parameters=NULL, expName="immunoClust Experiment", 
        history=NULL, state=NULL, label, B=50, tol=1e-5, modelName="mvt")

Arguments

data

A numeric matrix, data frame of observations, or object of class flowFrame.

parameters

A character vector specifying the parameters (columns) to be included in clustering. When it is left unspecified, all the parameters will be used.

expName

The name of the clustering experiment.

history

experimental; unused so far.

state

experimental: unused so far.

label

The N-dimensional vector containing the initial cluster membership. A label-number of 0 for an event indicates that this event is not initially assigned to a cluster.

B

The maximum number of EMt-iterations.

tol

The tolerance used to assess the convergence of the EMt-algorithms.

modelName

Used mixture model; either "mvt" or "mvn" for a t- or Gaussian mixture model respectively.

Value

The fitted clusters information in an object of class immunoClust.

Author(s)

Till Sörensen till-antoni.soerensen@charite.de

References

Sörensen, T., Baumgart, S., Durek, P., Grützkau, A. and Häupl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).

See Also

cell.EM

Examples

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data(dat.fcs)
data(dat.exp)
## cell.clustering result for dat.fcs
r1 <- dat.exp[[1]]
summary(r1)
## apply model parameter to all (unfiltered) events
dat.trans <- trans.ApplyToData(r1, dat.fcs)
r2 <- cell.ME(dat.trans, parameters=r1@parameters, label=r1@label)
summary(r2)

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